Breakpoint continuation for stream computing

ABSTRACT

A first stream operator in a stream computing application receives and processes a first stream of tuples. The processing at the first stream operator is paused in response to receiving a first one of the tuples in the first stream that triggers a breakpoint in the first stream operator. A determination of whether a condition to release the breakpoint is met is made, and the breakpoint is released in response to determining that the condition is met. The condition to release the breakpoint may be that a count of tuples of the first stream is outside of a threshold. A second stream of tuples may be received for processing at a second stream operator. The condition to release the breakpoint may be that a count of tuples of the second stream is outside of a threshold.

FIELD

This disclosure generally relates to stream computing, and inparticular, to the development of computing applications that receivestreaming data and process the data as it is received, includingtechniques for facilitating the debugging of a stream computingapplication.

BACKGROUND

Database systems are typically configured to separate the process ofstoring data from accessing, manipulating, or using data stored in adatabase. More specifically, database systems use a model in which datais first stored and indexed in a memory before subsequent querying andanalysis. In general, database systems may not be well suited forperforming real-time processing and analyzing streaming data. Inparticular, database systems may be unable to store, index, and analyzelarge amounts of streaming data efficiently or in real time.

Computer applications, such as database systems, include many computerprograms. The development of a computer program includes creating sourcecode, which may include many thousands of lines of instructions. Thesource code is converted into an executable program or machine codeusing a compiler. Computer programs, especially new ones, typicallycontain errors, commonly referred to as “bugs.” Debugging involvestesting and evaluating the computer program to find and correct errors.A programmer may use a computer program, commonly referred to as a“debugger” to assist in debugging a program. A debugger allows theprogrammer to execute a computer program under the control of thedebugger, allowing a process to be monitored.

SUMMARY

Embodiments of the disclosure provide a method, system, and computerprogram product for processing data. The method, system, and computerprogram product receive two or more tuples to be processed by aplurality of processing elements operating on one or more computerprocessors. In various embodiments, a method for debugging a streamcomputing application includes a first stream operator receiving a firststream of tuples. The first stream of tuples is processed at the firststream operator. The processing at the first stream operator is pausedin response to receiving a first one of the tuples in the first streamthat triggers a breakpoint in the first stream operator. A determinationof whether a condition to release the breakpoint is met is made, and thebreakpoint is released in response to determining that the condition ismet. The condition to release the breakpoint may be that a count oftuples of the first stream is outside of a threshold. A second stream oftuples may be received for processing at a second stream operator. Thecondition to release the breakpoint may be that a count of tuples of thesecond stream is outside of a threshold.

Other embodiments are directed to a computer program product and asystem.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a computing infrastructure configured to execute astream computing application according to various embodiments.

FIG. 2 illustrates a more detailed view of a compute node of FIG. 1according to various embodiments.

FIG. 3 illustrates a more detailed view of the management system of FIG.1 according to various embodiments.

FIG. 4 illustrates a more detailed view of the compiler system of FIG. 1according to various embodiments.

FIG. 5 illustrates an operator graph for a stream computing applicationaccording to various embodiments.

FIG. 6 illustrates a process for debugging a stream computingapplication according to various embodiments.

FIG. 7 illustrates a process for debugging a stream computingapplication according to various embodiments.

Like reference numbers and designations in the various drawings indicatelike elements.

DETAILED DESCRIPTION

Stream-based computing and stream-based database computing are emergingas a developing technology for database systems. Products are availablewhich allow users to create applications that process and querystreaming data before it reaches a database file. With this emergingtechnology, users can specify processing logic to apply to inbound datarecords while they are “in flight,” with the results available in a veryshort amount of time, often in fractions of a second. Constructing anapplication using this type of processing has opened up a newprogramming paradigm that will allow for development of a broad varietyof innovative applications, systems, and processes, as well as presentnew challenges for application programmers and database developers.

In a stream computing application, stream operators are connected to oneanother such that data flows from one stream operator to the next (e.g.,over a TCP/IP socket). When a stream operator receives data, it mayperform operations, such as analysis logic, which may change the tupleby adding or subtracting attributes, or updating the values of existingattributes within the tuple. When the analysis logic is complete, a newtuple is then sent to the next stream operator. Scalability is achievedby distributing an application across nodes by creating executables(i.e., processing elements), as well as replicating processing elementson multiple nodes and load balancing among them. Stream operators in astream computing application can be fused together to form a processingelement that is executable. Doing so allows processing elements to sharea common process space, resulting in much faster communication betweenstream operators than is available using inter-process communicationtechniques (e.g., using a TCP/IP socket). Further, processing elementscan be inserted or removed dynamically from an operator graphrepresenting the flow of data through the stream computing application.A particular stream operator may not reside within the same operatingsystem process as other stream operators. In addition, stream operatorsin the same operator graph may be hosted on different nodes, e.g., ondifferent compute nodes or on different cores of a compute node.

Data flows from one stream operator to another in the form of a “tuple.”A tuple is a sequence of one or more attributes associated with anentity. Attributes may be any of a variety of different types, e.g.,integer, float, Boolean, string, etc. The attributes may be ordered. Inaddition to attributes associated with an entity, a tuple may includemetadata, i.e., data about the tuple. A tuple may be extended by addingone or more additional attributes or metadata to it. As used herein,“stream” or “data stream” refers to a sequence of tuples. Generally, astream may be considered a pseudo-infinite sequence of tuples.

Tuples are received and output by stream operators and processingelements. An input tuple corresponding with a particular entity that isreceived by a stream operator or processing element, however, isgenerally not considered to be the same tuple that is output by thestream operator or processing element, even if the output tuplecorresponds with the same entity or data as the input tuple. An outputtuple need not be changed in some way from the input tuple.

Nonetheless, an output tuple may be changed in some way by a streamoperator or processing element. An attribute or metadata may be added,deleted, or modified. For example, a tuple will often have two or moreattributes. A stream operator or processing element may receive thetuple having multiple attributes and output a tuple corresponding withthe input tuple. The stream operator or processing element may onlychange one of the attributes so that all of the attributes of the outputtuple except one are the same as the attributes of the input tuple.

Generally, a particular tuple output by a stream operator or processingelement may not be considered to be the same tuple as a correspondinginput tuple even if the input tuple is not changed by the processingelement. However, to simplify the present description and the claims, anoutput tuple that has the same data attributes or is associated with thesame entity as a corresponding input tuple will be referred to herein asthe same tuple unless the context or an express statement indicatesotherwise.

Stream computing applications handle massive volumes of data that needto be processed efficiently and in real time. For example, a streamcomputing application may continuously ingest and analyze hundreds ofthousands of messages per second and up to petabytes of data per day.Accordingly, each stream operator in a stream computing application maybe required to process a received tuple within fractions of a second.

Like traditional database systems, developers of stream computingapplications need to identify the causes of bugs, poor performance, andsimilar issues. However, traditional debugging approaches may beinadequate to meet the needs of developers of stream computingapplications. This may be due in part to the fact that the processingelements in a stream environment are distributed across multiple nodesand computer systems, with each processing element running in a separateprocess. In addition, even though the processing elements are running inmany separate processes, there are typically significantinterdependencies between processing elements. Because of theinterdependencies, the cause of a bug may at a different node from thenode where erroneous results appear.

A conventional debugger allows the programmer to execute a computerprogram under the control of the debugger. A running computer program,i.e., a process, may be monitored with a debugger. The debugger may beused to determine the order in which instructions are executed. Thedebugger may be used to inspect the values of variables at variouspoints in program execution. Among other features, debuggers typicallysupport a breakpoint operation. Conventionally, a “breakpoint” functionpermits a programmer to set a breakpoint at a particular instruction orline. Program instructions are executed until the instruction with thebreakpoint is reached. Execution of the program is paused at theinstruction immediately preceding the breakpoint instruction and thevalues of variables at the breakpoint instruction may be presented tothe programmer for analysis. The debugger may be used to change thevalue of a variable of a paused process.

A conventional debugger may be used to debug the source code of anindividual stream operator running as a standalone application outsideof the streams environment. However, a bug may not appear when thestream operator running logic is running as a standalone application.The bug may only appear when all of the processing elements are runningin a streaming environment. Further, it may be difficult to determinewhich stream operator includes the logic that is causing the bug.Accordingly, there is a need to debug stream operators while the streamcomputing application is running.

One of the challenges of debugging a stream computing application is toallow the application to be debugged without interfering with the dataflow. If a debugger is used to set a breakpoint in a stream operator,triggering the breakpoint can substantially slowdown, disrupt, or haltdata flowing through portions of an operator graph. In addition, streamoperators downstream of the stream operator with the triggeredbreakpoint may be starved of input data. In other words, while a processin a particular stream operator is being debugged, the stream computingapplication may be prevented from running.

In various embodiments, a streams application is executing under thecontrol of a debugger. During execution, a first stream of tuples arereceived and processed at a first stream operator. A breakpoint may beset in the first stream operator. The processing may be paused at thefirst stream operator in response to receiving a first one of the tuplesin the first stream that triggers the breakpoint. While processing ispaused at the first stream operator, data may be flowing in other partsof the operator graph and other stream operators may process data. Inaddition, while processing is paused at the first stream operator, adetermination may be made as to whether a condition to release thebreakpoint is met. Moreover, the breakpoint may be automaticallyreleased in response to determining that the breakpoint is met.

Automatically releasing a breakpoint may minimize the amount of time thestream computing application may be prevented from running.Automatically releasing the breakpoint may permit the stream computingapplication to resume running when data is backing up in various partsof the operator graph or when various parts of the operator graph arestarved of data. Automatically releasing the breakpoint may permit thestream computing application to resume running when the data flow inanother part of the operator graph is of greater interest to theprogrammer than the part of the graph where the breakpoint wastriggered.

FIG. 1 illustrates one exemplary computing infrastructure 100 that maybe configured to execute a stream computing application, according tosome embodiments. The computing infrastructure 100 includes a managementsystem 105 and two or more compute nodes 110A-110D—i.e., hosts—which arecommunicatively coupled to each other using one or more communicationsnetworks 120. The communications network 120 may include one or moreservers, networks, or databases, and may use a particular communicationprotocol to transfer data between the compute nodes 110A-110D. Adevelopment system 102 may be communicatively coupled with themanagement system 105 and the compute nodes 110 either directly or viathe communications network 120.

The communications network 120 may include a variety of types ofphysical communication channels or “links.” The links may be wired,wireless, optical, or any other suitable media. In addition, thecommunications network 120 may include a variety of network hardware andsoftware for performing routing, switching, and other functions, such asrouters, switches, or bridges. The communications network 120 may bededicated for use by a stream computing application or shared with otherapplications and users. The communications network 120 may be any size.For example, the communications network 120 may include a single localarea network or a wide area network spanning a large geographical area,such as the Internet.

FIG. 2 is a more detailed view of a compute node 110, which may be thesame as one of the compute nodes 110A-110D of FIG. 1, according tovarious embodiments. The compute node 110 may include, withoutlimitation, one or more processors (CPUs) 205, a network interface 215,an interconnect 220, a memory 225, and a storage 230. The compute node110 may also include an I/O device interface 210 used to connect I/Odevices 212, e.g., keyboard, display, and mouse devices, to the computenode 110.

Each CPU 205 retrieves and executes programming instructions stored inthe memory 225 or storage 230. Similarly, the CPU 205 stores andretrieves application data residing in the memory 225. The interconnect220 is used to transmit programming instructions and application databetween each CPU 205, I/O device interface 210, storage 230, networkinterface 215, and memory 225. The interconnect 220 may be one or morebusses. The CPUs 205 may be a single CPU, multiple CPUs, or a single CPUhaving multiple processing cores in various embodiments. In oneembodiment, a processor 205 may be a digital signal processor (DSP). Oneor more processing elements 235 (described below) may be stored in thememory 225. A processing element 235 may include one or more streamoperators 240 (described below). In one embodiment, a processing element235 is assigned to be executed by only one CPU 205, although in otherembodiments the stream operators 240 of a processing element 235 mayinclude one or more threads that are executed on two or more CPUs 205.The memory 225 is generally included to be representative of a randomaccess memory, e.g., Static Random Access Memory (SRAM), Dynamic RandomAccess Memory (DRAM), or Flash. The storage 230 is generally included tobe representative of a non-volatile memory, such as a hard disk drive,solid state device (SSD), or removable memory cards, optical storage,flash memory devices, network attached storage (NAS), or connections tostorage area network (SAN) devices, or other devices that may storenon-volatile data. The network interface 215 is configured to transmitdata via the communications network 120.

A stream computing application may include one or more stream operators240 that may be compiled into a “processing element” container 235. Thememory 225 may include two or more processing elements 235, eachprocessing element having one or more stream operators 240. Each streamoperator 240 may include a portion of code that processes tuples flowinginto a processing element and outputs tuples to other stream operators240 in the same processing element, in other processing elements, or inboth the same and other processing elements in a stream computingapplication. Processing elements 235 may pass tuples to other processingelements that are on the same compute node 110 or on other compute nodesthat are accessible via communications network 120. For example, aprocessing element 235 on compute node 110A may output tuples to aprocessing element 235 on compute node 110B.

The storage 230 may include a buffer 260 for storing buffered streamdata 260. The buffer 260 represents a storage space for data flowinginto the compute node 110 from upstream processing elements (or from adata source for the stream application). For example, buffered streamdata may include data tuples waiting to be processed by one of the PEs235. Buffered stream data may also store the results of data processingperformed by PEs 235 that will be sent to downstream processing elements(or load shed). Although shown as being in storage, the buffer 260 maybe located in the memory 225 of the compute node 110 or in a combinationof both memories. Moreover, storage 230 may include storage space thatis external to the compute node 110, such as in a cloud.

The compute node 110 may include one or more operating systems 262. Anoperating system 262 may be stored partially in memory 225 and partiallyin storage 230. Alternatively, an operating system may be storedentirely in memory 225 or entirely in storage 230. The operating systemprovides an interface between various hardware resources, including theCPU 205, and processing elements and other components of the streamcomputing application. In addition, an operating system provides commonservices for application programs, such as providing a time function.

FIG. 3 is a more detailed view of the management system 105 of FIG. 1according to some embodiments. The management system 105 may include,without limitation, one or more processors (CPUs) 305, a networkinterface 315, an interconnect 320, a memory 325, and a storage 330. Themanagement system 105 may also include an I/O device interface 310connecting I/O devices 312, e.g., keyboard, display, and mouse devices,to the management system 105.

Each CPU 305 retrieves and executes programming instructions stored inthe memory 325 or storage 330. Similarly, each CPU 305 stores andretrieves application data residing in the memory 325 or storage 330.The interconnect 320 is used to move data, such as programminginstructions and application data, between the CPU 305, I/O deviceinterface 310, storage unit 330, network interface 315, and memory 325.The interconnect 320 may be one or more busses. The CPUs 305 may be asingle CPU, multiple CPUs, or a single CPU having multiple processingcores in various embodiments. In one embodiment, a processor 305 may bea DSP. Memory 325 is generally included to be representative of a randomaccess memory, e.g., SRAM, DRAM, or Flash. The storage 330 is generallyincluded to be representative of a non-volatile memory, such as a harddisk drive, solid state device (SSD), removable memory cards, opticalstorage, Flash memory devices, network attached storage (NAS),connections to storage area-network (SAN) devices, or the cloud. Thenetwork interface 315 is configured to transmit data via thecommunications network 120.

The memory 325 may store a stream manager 134. Additionally, the storage330 may store an operator graph 335. The operator graph 335 may definehow tuples are routed to processing elements 235 (FIG. 2) forprocessing.

The management system 105 may include one or more operating systems 332.An operating system 332 may be stored partially in memory 325 andpartially in storage 330. Alternatively, an operating system may bestored entirely in memory 325 or entirely in storage 330. The operatingsystem provides an interface between various hardware resources,including the CPU 305, and processing elements and other components ofthe stream computing application. In addition, an operating systemprovides common services for application programs, such as providing atime function.

FIG. 4 is a more detailed view of the development system 102 of FIG. 1according to some embodiments. The development system 102 may include,without limitation, one or more processors (CPUs) 405, a networkinterface 415, an interconnect 420, a memory 425, and storage 430. Thedevelopment system 102 may also include an I/O device interface 410connecting I/O devices 412, e.g., keyboard, display, and mouse devices,to the development system 102.

Each CPU 405 retrieves and executes programming instructions stored inthe memory 425 or storage 430. Similarly, each CPU 405 stores andretrieves application data residing in the memory 425 or storage 430.The interconnect 420 is used to move data, such as programminginstructions and application data, between the CPU 405, I/O deviceinterface 410, storage unit 430, network interface 415, and memory 425.The interconnect 420 may be one or more busses. The CPUs 405 may be asingle CPU, multiple CPUs, or a single CPU having multiple processingcores in various embodiments. In one embodiment, a processor 405 may bea DSP. Memory 425 is generally included to be representative of a randomaccess memory, e.g., SRAM, DRAM, or Flash. The storage 430 is generallyincluded to be representative of a non-volatile memory, such as a harddisk drive, solid state device (SSD), removable memory cards, opticalstorage, flash memory devices, network attached storage (NAS),connections to storage area-network (SAN) devices, or to the cloud. Thenetwork interface 415 is configured to transmit data via thecommunications network 120.

The development system 102 may include one or more operating systems432. An operating system 432 may be stored partially in memory 425 andpartially in storage 430. Alternatively, an operating system may bestored entirely in memory 425 or entirely in storage 430. The operatingsystem provides an interface between various hardware resources,including the CPU 405, and processing elements and other components ofthe stream computing application. In addition, an operating systemprovides common services for application programs, such as providing atime function.

The memory 425 may store a compiler 136. The compiler 136 compilesmodules, which include source code or statements, into the object code,which includes machine instructions that execute on a processor. In oneembodiment, the compiler 136 may translate the modules into anintermediate form before translating the intermediate form into objectcode. The compiler 136 may output a set of deployable artifacts that mayinclude a set of processing elements and an application descriptionlanguage file (ADL file), which is a configuration file that describesthe stream computing application. In some embodiments, the compiler 136may be a just-in-time compiler that executes as part of an interpreter.In other embodiments, the compiler 136 may be an optimizing compiler. Invarious embodiments, the compiler 136 may perform peepholeoptimizations, local optimizations, loop optimizations, inter-proceduralor whole-program optimizations, machine code optimizations, or any otheroptimizations that reduce the amount of time required to execute theobject code, to reduce the amount of memory required to execute theobject code, or both. In various embodiments, the compiler 136 maygenerate object code in a form that facilitates debugging. The output ofthe compiler 136 may be represented by an operator graph, e.g., theoperator graph 335.

The compiler 136 may also provide the application administrator with theability to optimize performance through profile-driven fusionoptimization. Fusing operators may improve performance by reducing thenumber of calls to a transport. While fusing stream operators mayprovide faster communication between operators than is available usinginter-process communication techniques, any decision to fuse operatorsrequires balancing the benefits of distributing processing acrossmultiple compute nodes with the benefit of faster inter-operatorcommunications. The compiler 136 may automate the fusion process todetermine how to best fuse the operators to be hosted by one or moreprocessing elements, while respecting user-specified constraints. Thismay be a two-step process, including compiling the application in aprofiling mode and running the application, then re-compiling and usingthe optimizer during this subsequent compilation. The end result may,however, be a compiler-supplied deployable application with an optimizedapplication configuration.

The memory 425 may also store a stream debugger 138. The stream debugger138 may be used to manage the debugging of a processing element (PE) orstream operator on one (or more) of the compute nodes 110. For example,the stream debugger 138 may be used to set breakpoints, to executeinstructions to step into (or over) function calls in the instructions,to inspect variables, etc., as well as provide a variety of otherfunctions and features used for debugging the processing elements 235 orstream operators 240. For example, the stream debugger 138 may determinewhether a condition to release a breakpoint has been met. The streamdebugger 138 may automatically release the breakpoint in response todetermining that a condition has been met. In various embodiments, theremay be a variety of conditions to release the breakpoint.

The condition to release the breakpoint may be that a count of tuples atthe stream operator where the breakpoint is triggered is outside of athreshold. In an embodiment, a breakpoint may be set and triggered in afirst stream operator that receives as input a first stream of tuples.The condition to release the breakpoint may be that a count of tuples inthe first stream is outside of a threshold.

The condition to release the breakpoint may be that a count of tuples ata stream operator different from the one where the breakpoint istriggered is outside of a threshold. In an embodiment, the breakpointmay be set and triggered in a first stream operator that receives asinput a first stream of tuples. The condition to release the breakpointmay be that a count of tuples in a second stream that is input to asecond stream operator is outside of a threshold.

The condition to release the breakpoint may be that a count associatedwith a group of stream operators in an operator graph is outside of athreshold. In an embodiment, the breakpoint may be set and triggered ina first stream operator that receives as input a first stream of tuples.A second stream of tuples may be received to be processed at a secondstream operator. A third stream of tuples may be received to beprocessed at a third stream operator. The condition to release thebreakpoint may be that a first count of tuples of the second stream isoutside of a first threshold or a second count of tuples of the thirdstream is outside of a second threshold, or that both the first andsecond counts are outside of their respective thresholds.

The condition to release the breakpoint may be based on a rate of tuplesreceived at the stream operator where the breakpoint is triggered. Thecondition to release the breakpoint may be that the rate is outside of athreshold. The rate may be expressed as a range, e.g., if tuples areflowing into a stream operator in a range greater than 1,000 tuples perminute, the breakpoint will be released. In an embodiment, thebreakpoint may be set and triggered in a first stream operator thatreceives as input a first stream of tuples. The condition to release thebreakpoint may be that a quantity of tuples of the first stream per unitof time is outside of a threshold.

The condition to release the breakpoint may be based on a rate of tuplesreceived at a stream operator different from the one where thebreakpoint is triggered. The condition to release the breakpoint may bethat the rate is outside of a threshold. In an embodiment, thebreakpoint may be set and triggered in a first stream operator thatreceives as input a first stream of tuples. The condition to release thebreakpoint is that a quantity of tuples per unit of time of a secondstream input to a second stream operator is outside of a threshold.

The condition to release the breakpoint may be based on analyzing one ormore attributes of tuples in an input stream of tuples. For example, afirst attribute may be an integer value. The first attribute of eachreceived tuple may be analyzed to determine if the attribute data isgreater than a particular value. If the first attribute is greater thanthe value, a count is incremented. The condition to release thebreakpoint is met when a count of tuples having a first attributegreater than the particular value exceeds a count threshold. In anembodiment, the breakpoint may be set and triggered in a first streamoperator that receives as input a first stream of tuples. The conditionto release the breakpoint may include determining whether a firstattribute of a tuple of the first stream meets an attribute condition,e.g., the first attribute is outside of an attribute threshold. Thecondition to release the breakpoint may be that a count of tuples of thefirst stream that meet the attribute condition is outside of a countthreshold.

The condition to release the breakpoint may be based on a change in therate of tuples received at the stream operator where the breakpoint istriggered or at another stream operator. For example, tuples of a firststream are received at first rate. Subsequently, tuples of a firststream are received at second rate. The difference between the first andsecond rates is determined. If the difference is outside of a threshold,the breakpoint is released. In an embodiment, the condition to releasethe breakpoint is based on a first quantity of tuples of the firststream per unit of time changing to a second quantity of tuples of thefirst stream per unit of time. The condition to release the breakpointis that a difference between the first quantity of tuples of the firststream per unit of time and the second quantity of tuples of the firststream per unit of time is outside of a rate difference threshold.

FIG. 5 illustrates an exemplary operator graph 500 for a streamcomputing application beginning from one or more sources 135 through toone or more sinks 504, 506, according to some embodiments. This flowfrom source to sink may also be generally referred to herein as anexecution path. In addition, a flow from one processing element toanother may be referred to as an execution path in various contexts.Although FIG. 5 is abstracted to show connected processing elementsPE1-PE10, the operator graph 500 may include data flows between streamoperators 240 (FIG. 2) within the same or different processing elements.Typically, processing elements, such as processing element 235 (FIG. 2),receive tuples from the stream as well as output tuples into the stream(except for a sink—where the stream terminates, or a source—where thestream begins). While the operator graph 500 includes a relatively smallnumber of components, an operator graph may be much more complex and mayinclude many individual operator graphs that may be statically ordynamically linked together.

The example operator graph shown in FIG. 5 includes ten processingelements (labeled as PE1-PE10) running on the compute nodes 110A-110D. Aprocessing element may include one or more stream operators fusedtogether to form an independently running process with its own processID (PID) and memory space. In cases where two (or more) processingelements are running independently, inter-process communication mayoccur using a “transport,” e.g., a network socket, a TCP/IP socket, orshared memory. Inter-process communication paths used for inter-processcommunications can be a critical resource in a stream computingapplication. However, when stream operators are fused together, thefused stream operators can use more rapid communication techniques forpassing tuples among stream operators in each processing element.

The operator graph 500 begins at a source 135 and ends at a sink 504,506. Compute node 110A includes the processing elements PE1, PE2, andPE3. Source 135 flows into the processing element PE1, which in turnoutputs tuples that are received by PE2 and PE3. For example, PE1 maysplit data attributes received in a tuple and pass some data attributesin a new tuple to PE2, while passing other data attributes in anothernew tuple to PE3. As a second example, PE1 may pass some received tuplesto PE2 while passing other tuples to PE3. Tuples that flow to PE2 areprocessed by the stream operators contained in PE2, and the resultingtuples are then output to PE4 on compute node 110B. Likewise, the tuplesoutput by PE4 flow to operator sink PE6 504. Similarly, tuples flowingfrom PE3 to PE5 also reach the operators in sink PE6 504. Thus, inaddition to being a sink for this example operator graph, PE6 could beconfigured to perform a join operation, combining tuples received fromPE4 and PE5. This example operator graph also shows tuples flowing fromPE3 to PE7 on compute node 110C, which itself shows tuples flowing toPE8 and looping back to PE7. Tuples output from PE8 flow to PE9 oncompute node 110D, which in turn outputs tuples to be processed byoperators in a sink processing element, for example PE10 506.

Some representative data flows or streams between processing elementsare labeled in FIG. 5. Stream S1 represents a stream flowing from PE5 toPE6. Stream S2 represents a stream flowing from PE3 to PE5. Stream S3represents a stream flowing from PE2 to PE4. Stream S4 represents astream flowing from PE4 to PE6. Stream S5 represents a stream flowingfrom PE3 to PE7. Stream S6 represents a stream flowing from PE1 to PE2.Stream S7 represents a stream flowing from PE1 to PE3. Stream S8represents a stream flowing from PE8 to PE9. The streams depicted inFIG. 5 do not include streams that may flow between stream operatorswithin a processing element, nor are they intended to be an exhaustiverepresentation of all streams in the operator graph 500.

Processing elements 235 (FIG. 2) may be configured to receive or outputtuples in various formats, e.g., the processing elements or streamoperators could exchange data marked up as XML documents. Furthermore,each stream operator 240 within a processing element 235 may beconfigured to carry out any form of data processing functions onreceived tuples, including, for example, writing to database tables orperforming other database operations such as data joins, splits, reads,etc., as well as performing other data analytic functions or operations.

The stream manager 134 of FIG. 1 may be configured to monitor a streamcomputing application running on compute nodes, e.g., compute nodes110A-110D, as well as to change the deployment of an operator graph,e.g., operator graph 132. The stream manager 134 may move processingelements from one compute node 110 to another, for example, to managethe processing loads of the compute nodes 110A-110D in the computinginfrastructure 100. Further, stream manager 134 may control the streamcomputing application by inserting, removing, fusing, un-fusing, orotherwise modifying the processing elements and stream operators (orwhat tuples flow to the processing elements) running on the computenodes 110A-110D.

Because a processing element may be a collection of fused streamoperators, it is equally correct to describe the operator graph as oneor more execution paths between specific stream operators, which mayinclude execution paths to different stream operators within the sameprocessing element. FIG. 5 illustrates execution paths betweenprocessing elements for the sake of clarity. The execution paths arecoextensive with the shown streams.

FIG. 6 illustrates a process 600 for debugging a stream computingapplication according to various embodiments. A stream debugger receivesan indication of a breakpoint for a stream operator at operation 602.The breakpoint is set at the indicated stream operator. The streamcomputing application is then run under the control of the streamdebugger. At operation 604, a tuple is received at the stream operatorwhere the breakpoint is set. It is determined in operation 606 whetherthe received tuple causes the breakpoint to be triggered. A breakpointmay be triggered because the received tuple causes a particular line ofcode to be executed. In addition, a breakpoint may be triggered becausea condition is met. For example, an attribute of the received tuple maymeet a condition, e.g., the attribute is of a particular size or type.If the received tuple does not cause the breakpoint to be triggered, thetuple may be processed in operation 608. After processing the tuple, theprocess 600 returns to operation 604, where a next tuple is received. Ifthe received tuple causes the breakpoint to be triggered, the process600 moves to operation 610.

In operation 610, a count may be initialized, e.g., set to zero. Inoperation 612, the count is incremented in response to receiving (ortransmitting) a tuple at one or more locations in an operator graph, asfurther described below. (In an alternative, the count may beinitialized to an integer and decremented in operation 612.) The countis compared with a threshold associated with a warning in operation 614.If the count is less than the warning threshold, the count is comparedwith a threshold associated with releasing the breakpoint in operation618. If the count is less than the breakpoint release threshold, thetuple is “handled” in operation 620, as described below. After handling,a next tuple is received in operation 622 and the process 600 returns tooperation 612, where the count is incremented for the next receivedtuple.

If the count is determined to be outside (e.g., greater than or equalto) the warning threshold in operation 614, a warning indication may beprovided to a programmer in operation 616. The warning indication mayindicate how much longer the breakpoint will be held before release.

If the count is outside (e.g., greater than or equal to) the breakpointrelease threshold, the breakpoint is automatically released in operation624. Automatically releasing the breakpoint may minimize the amount oftime the stream computing application may be prevented from running.Automatically releasing the breakpoint may permit the stream computingapplication to resume running when data is backing up in various partsof the operator graph or when various parts of the operator graph arestarved of data. Automatically releasing the breakpoint may permit thestream computing application to resume running when the data flow inanother part of the operator graph is of greater interest to theprogrammer than the part of the graph where the breakpoint wastriggered.

As mentioned, the count is incremented in operation 612. In anembodiment, the count may be a count of tuples received at the streamoperator where the breakpoint is triggered. For example, if the streamoperator where the breakpoint is triggered is in PE6, the count may be acount of tuples in one or both of the streams S1 or S4 (FIG. 5). Inanother embodiment, the count may be a count of tuples at a streamoperator different from the one where the breakpoint is triggered. Forexample, if the stream operator where the breakpoint is triggered is inPE6, the count may be a count of tuples in the stream S2, stream S3,stream S5, stream S6, stream S7, or stream S8 (FIG. 5). In yet anotherembodiment, the count may be a count of tuples associated with a groupof stream operators. For example, the stream operators in PE2 and PE3may be a group of streams operators. The count may be counts of tuplesin the stream S2, stream S3, and stream S5. In this embodiment, thecount for each stream may be compared with warning and breakpointrelease thresholds (in operations 614 and 618) that may be unique foreach stream.

A tuple may be handled in operation 620 in a variety of ways accordingto various embodiments. In an embodiment, a currently received tuple maybe processed by a stream operator in operation 620. For example, if thecount is of tuples at a stream operator different from the one where thebreakpoint is triggered, that other stream operator may process thereceived tuple. In an embodiment, a currently received tuple may bestored in operation 620. For example, if the count is a count of tuplesreceived at the stream operator where the breakpoint is triggered,received tuples may be stored in the buffer 260. In an embodiment, acurrently received tuple may be shed, i.e., discarded, in operation 620.In an embodiment, a currently received tuple may be routed to anotherstream operator for processing in operation 620.

FIG. 7 illustrates a process 700 for debugging a stream computingapplication according to various embodiments. The process 700 ispremised on a breakpoint first being set in a stream operator in thestream computing application, for example, as describe in process 600.In operation 704, it is determined whether a received tuple causes thebreakpoint to be triggered. If the breakpoint is triggered, a count maybe initialized in operation 704 in the same manner as in operation 610.In operation 705, the count is incremented in response to receiving (ortransmitting) a tuple at one or more locations in an operator graph inthe same manner as in operation 612. In operation 708, the count is useto determine a rate, i.e., a quantity of tuples of the second stream perunit of time. For example, operation 708 may determine a rate in termsof tuples per minute.

The rate is compared with a rate threshold associated with a warning inoperation 710. If the rate is determined to be outside of (e.g., greaterthan or equal to) the rate warning threshold in operation 710, a warningindication may be provided to a programmer in operation 712. If the rateis not determined to be outside the warning threshold in operation 710,it is determined, in operation 714, whether the rate is outside of athreshold associated with releasing the breakpoint. If the rate isoutside of the breakpoint rate release threshold, the tuple may be“handled” in operation 716. The operation 716 may be the same as theoperation 620. After handling, a next tuple is received in operation 718and the process 700 returns to operation 706, where the count isincremented for the next received tuple. The operation 718 may be thesame as the operation 622.

In an embodiment, the process 700 may include one or more operations(not shown) in which a rate determined in operation 708 is saved andcompared with a rate determined in operation 708 at a later time. Forexample, a first rate may be determined at a first time and a secondrate may be determined at a second time, the second time beingsubsequent to the first time. A difference between the first and secondrates may be determined and compared with a difference threshold. If thedifference is outside of the difference threshold, the breakpoint isrelased.

In the foregoing, reference is made to various embodiments. It should beunderstood, however, that this disclosure is not limited to thespecifically described embodiments. Instead, any combination of thedescribed features and elements, whether related to differentembodiments or not, is contemplated to implement and practice thisdisclosure. Furthermore, although embodiments of this disclosure mayachieve advantages over other possible solutions or over the prior art,whether or not a particular advantage is achieved by a given embodimentis not limiting of this disclosure. Thus, the described aspects,features, embodiments, and advantages are merely illustrative and arenot considered elements or limitations of the appended claims exceptwhere explicitly recited in a claim(s).

Aspects of the present disclosure may be embodied as a system, method,or computer program product. Accordingly, aspects of the presentdisclosure may take the form of an entirely hardware embodiment, anentirely software embodiment (including firmware, resident software,micro-code, etc.), or an embodiment combining software and hardwareaspects that may all generally be referred to herein as a “circuit,”“module,” or “system.” Furthermore, aspects of the present disclosuremay take the form of a computer program product embodied in one or morecomputer readable medium(s) having computer readable program codeembodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readable signalmedium or a computer readable storage medium. A computer readablestorage medium may be, for example, but not limited to, an electronic,magnetic, optical, electromagnetic, infrared, or semiconductor system,apparatus, or device, or any suitable combination thereof. More specificexamples (a non-exhaustive list) of the computer readable storage mediumwould include the following: an electrical connection having one or morewires, a portable computer diskette, a hard disk, a random access memory(RAM), a read-only memory (ROM), an erasable programmable read-onlymemory (EPROM or Flash memory), an optical fiber, a portable compactdisc read-only memory (CD-ROM), an optical storage device, a magneticstorage device, or any suitable combination thereof. In the context ofthis disclosure, a computer readable storage medium may be any tangiblemedium that can contain, or store, a program for use by or in connectionwith an instruction execution system, apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wire line, optical fiber cable, RF, etc., or any suitable combinationthereof.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including: (a) an object oriented programminglanguage; (b) conventional procedural programming languages; and (c) astreams programming language, such as IBM Streams Processing Language(SPL). The program code may execute as specifically described herein. Inaddition, the program code may execute entirely on the user's computer,partly on the user's computer, as a stand-alone software package, partlyon the user's computer and partly on a remote computer, or entirely onthe remote computer or server. In the latter scenario, the remotecomputer may be connected to the user's computer through any type ofnetwork, including a local area network (LAN) or a wide area network(WAN), or the connection may be made to an external computer (forexample, through the Internet using an Internet Service Provider).

Aspects of the present disclosure have been described with reference toflowchart illustrations, block diagrams, or both, of methods,apparatuses (systems), and computer program products according toembodiments of this disclosure. It will be understood that each block ofthe flowchart illustrations or block diagrams, and combinations ofblocks in the flowchart illustrations or block diagrams, can beimplemented by computer program instructions. These computer programinstructions may be provided to a processor of a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor of the computer or other programmabledata processing apparatus, create means for implementing the functionsor acts specified in the flowchart or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function or act specified in the flowchart or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus, or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions or acts specified in the flowchart or blockdiagram block or blocks.

Embodiments according to this disclosure may be provided to end-usersthrough a cloud-computing infrastructure. Cloud computing generallyrefers to the provision of scalable computing resources as a serviceover a network. More formally, cloud computing may be defined as acomputing capability that provides an abstraction between the computingresource and its underlying technical architecture (e.g., servers,storage, networks), enabling convenient, on-demand network access to ashared pool of configurable computing resources that can be rapidlyprovisioned and released with minimal management effort or serviceprovider interaction. Thus, cloud computing allows a user to accessvirtual computing resources (e.g., storage, data, applications, and evencomplete virtualized computing systems) in “the cloud,” without regardfor the underlying physical systems (or locations of those systems) usedto provide the computing resources.

Typically, cloud-computing resources are provided to a user on apay-per-use basis, where users are charged only for the computingresources actually used (e.g., an amount of storage space used by a useror a number of virtualized systems instantiated by the user). A user canaccess any of the resources that reside in the cloud at any time, andfrom anywhere across the Internet. In context of the present disclosure,a user may access applications or related data available in the cloud.For example, the nodes used to create a stream computing application maybe virtual machines hosted by a cloud service provider. Doing so allowsa user to access this information from any computing system attached toa network connected to the cloud (e.g., the Internet).

The flowchart and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present disclosure. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams or flowchart illustration, andcombinations of blocks in the block diagrams or flowchart illustration,can be implemented by special purpose hardware-based systems thatperform the specified functions or acts, or combinations of specialpurpose hardware and computer instructions.

Although embodiments are described within the context of a streamcomputing application, this is not the only context relevant to thepresent disclosure. Instead, such a description is without limitationand is for illustrative purposes only. Additional embodiments may beconfigured to operate with any computer system or application capable ofperforming the functions described herein. For example, embodiments maybe configured to operate in a clustered environment with a standarddatabase processing application. A multi-nodal environment may operatein a manner that effectively processes a stream of tuples. For example,some embodiments may include a large database system, and a query of thedatabase system may return results in a manner similar to a stream ofdata.

While the foregoing is directed to exemplary embodiments, other andfurther embodiments of the disclosure may be devised without departingfrom the basic scope thereof, and the scope thereof is determined by theclaims that follow.

1. A method for debugging a stream computing application having aplurality of processing elements operating on one or more computerprocessors, each processing element having one or more stream operators,comprising: receiving a first stream of tuples at a first streamoperator; processing the first stream of tuples at the first streamoperator; pausing the processing at the first stream operator inresponse to receiving a first one of the tuples in the first stream thattriggers a breakpoint in the first stream operator; determining whethera condition to release the breakpoint is met; and releasing thebreakpoint in response to determining that the condition is met.
 2. Themethod of claim 1, wherein the condition to release the breakpoint isthat a count of tuples of the first stream is outside of a threshold. 3.The method of claim 1, further comprising receiving a second stream oftuples to be processed at a second stream operator, wherein thecondition to release the breakpoint is that a count of tuples of thesecond stream is outside of a threshold.
 4. The method of claim 1,further comprising receiving a second stream of tuples to be processedat a second stream operator and a third stream of tuples to be processedat a third stream operator, wherein the condition to release thebreakpoint is that one of first and second counts of tuples of thesecond and third streams is outside of respective first and secondthresholds.
 5. The method of claim 1, wherein the condition to releasethe breakpoint is that a quantity of tuples of the first stream per unitof time is outside of a threshold.
 6. The method of claim 1, furthercomprising receiving a second stream of tuples to be processed at asecond stream operator, wherein the condition to release the breakpointis that a quantity of tuples of the second stream per unit of time isoutside of a threshold.
 7. The method of claim 1, further comprisingdetermining whether a first attribute of the tuple meets an attributecondition, wherein the condition to release the breakpoint is that acount of tuples of the first stream that meet the attribute condition isoutside of a threshold.
 8. The method of claim 1, wherein the conditionto release the breakpoint is that a first quantity of tuples of thefirst stream per unit of time changes to a second quantity of tuples ofthe first stream per unit of time, and a difference between the firstquantity of tuples of the first stream per unit of time and the secondquantity of tuples of the first stream per unit of time is outside of athreshold.
 9. A computer program product for processing a stream oftuples, the computer program product comprising a computer readablestorage medium having program code embodied therewith, the program codecomprising computer readable program code configured to facilitatedebugging of a stream computing application having a plurality ofprocessing elements operating on one or more computer processors, eachprocessing element having one or more stream operators, comprising:receiving a first stream of tuples at a first stream operator;processing the first stream of tuples at the first stream operator;pausing the processing at the first stream operator in response toreceiving a first one of the tuples in the first stream that triggers abreakpoint in the first stream operator; determining whether a conditionto release the breakpoint is met; and releasing the breakpoint inresponse to determining that the condition is met.
 10. The computerprogram product of claim 9, wherein the condition to release thebreakpoint is that a count of tuples of the first stream is outside of athreshold.
 11. The computer program product of claim 9, furthercomprising receiving a second stream of tuples to be processed at asecond stream operator, wherein the condition to release the breakpointis that a count of tuples of the second stream is outside of athreshold.
 12. The computer program product of claim 9, furthercomprising receiving a second stream of tuples to be processed at asecond stream operator and a third stream of tuples to be processed at athird stream operator, wherein the condition to release the breakpointis that one of first and second counts of tuples of the second and thirdstreams is outside of respective first and second thresholds.
 13. Thecomputer program product of claim 9, wherein the condition to releasethe breakpoint is that a quantity of tuples of the first stream per unitof time is outside of a threshold.
 14. The computer program product ofclaim 9, further comprising receiving a second stream of tuples to beprocessed at a second stream operator, wherein the condition to releasethe breakpoint is that a quantity of tuples of the second stream perunit of time is outside of a threshold.
 15. The computer program productof claim 9, further comprising determining whether a first attribute ofthe tuple meets an attribute condition, wherein the condition to releasethe breakpoint is that a count of tuples of the first stream that meetthe attribute condition is outside of a threshold.
 16. The computerprogram product of claim 9, wherein the condition to release thebreakpoint is that a first quantity of tuples of the first stream perunit of time changes to a second quantity of tuples of the first streamper unit of time, and a difference between the first quantity of tuplesof the first stream per unit of time and the second quantity of tuplesof the first stream per unit of time is outside of a threshold. 17-20.(canceled)